Summary: The Postgres avg() function computes the arithmetic mean of a numeric column or expression, returning numeric for integer/numeric inputs and double precision for floating-point inputs. Use it when you need grouped averages with GROUP BY, conditional averages with FILTER, moving averages as a window function, or row-vs-mean comparisons via subquery. avg() automatically skips NULL values and works as both an aggregate and a window function.

Calculate the average value of a set of numbers

The Postgres avg() function calculates the arithmetic mean of a set of numeric values.

Use it when you need to understand typical values in a dataset, compare different groups, or identify trends over time. For example, you might use it to calculate the average order value for an e-commerce platform, the average response time for a web service, or the mean of sensor readings over time.

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The avg() function has the simple form:

avg(expression) -> numeric type

• expression: Any numeric expression or column name whose average you want to calculate.

The avg() function returns an output of the type numeric when applied to integer or numeric values. When used with floating-point values, the output type is double precision.

Consider a table weather_data tracking the temperature readings for different cities. It has the columns date, city and temperature. We will use the avg() function to analyze this data.

CREATE TABLE weather_data (
  date DATE,
  city TEXT,
  temperature NUMERIC
);

INSERT INTO weather_data (date, city, temperature) VALUES
  ('2024-03-01', 'New York', 5.5),
  ('2024-03-01', 'Los Angeles', 22.0),
  ('2024-03-01', 'Chicago', 2.0),
  ('2024-03-02', 'New York', 7.0),
  ('2024-03-02', 'Los Angeles', 23.5),
  ('2024-03-02', 'Chicago', 3.5),
  ('2024-03-03', 'New York', 6.5),
  ('2024-03-03', 'Los Angeles', 21.5),
  ('2024-03-03', 'Chicago', 1.0);

To calculate the average temperature reading across all cities and dates, you can use the following query:

SELECT avg(temperature) AS avg_temperature
FROM weather_data;

This query computes the average of all values in the temperature column.

   avg_temperature
---------------------
 10.2777777777777778
(1 row)

You can use avg() with a GROUP BY clause to calculate averages for different cities:

SELECT city, avg(temperature) AS avg_temperature
FROM weather_data
GROUP BY city
ORDER BY avg_temperature DESC;

This query returns the average temperature recorded for each city, ordered by the highest average temperature:

    city     |   avg_temperature
-------------+---------------------
 Los Angeles | 22.3333333333333333
 New York    |  6.3333333333333333
 Chicago     |  2.1666666666666667
(3 rows)

Postgres allows you to use a FILTER clause with aggregate functions to selectively include rows in the calculation:

SELECT
  city,
  avg(temperature) as avg_temperature,
  avg(temperature) FILTER (WHERE date >= '2024-03-03') AS avg_temperature_since_3rd
FROM weather_data
GROUP BY city;

This query calculates the average temperature for each city and the average temperature since March 3rd, 2024.

    city     |   avg_temperature   | avg_temperature_since_3rd
-------------+---------------------+---------------------------
 Chicago     |  2.1666666666666667 |    1.00000000000000000000
 Los Angeles | 22.3333333333333333 |       21.5000000000000000
 New York    |  6.3333333333333333 |        6.5000000000000000
(3 rows)

You can use avg() in a subquery to compare individual values against the average:

WITH temp_diff AS (
  SELECT
    date,
    city,
    temperature,
    temperature - (SELECT avg(temperature) FROM weather_data) AS temp_diff_from_avg
  FROM weather_data
)
SELECT *
FROM temp_diff
ORDER BY abs(temp_diff_from_avg) DESC
LIMIT 5;

This query calculates the difference between each temperature reading and the overall average temperature, and returns the top 5 records with the largest deviations:

    date    |    city     | temperature | temp_diff_from_avg
------------+-------------+-------------+---------------------
 2024-03-02 | Los Angeles |        23.5 | 13.2222222222222222
 2024-03-01 | Los Angeles |        22.0 | 11.7222222222222222
 2024-03-03 | Los Angeles |        21.5 | 11.2222222222222222
 2024-03-02 | New York    |         7.0 | -3.2777777777777778
 2024-03-03 | New York    |         6.5 | -3.7777777777777778
(5 rows)

We can use avg() as a window function to calculate a moving average over the specified window of rows.

SELECT
  date,
  city,
  temperature,
  avg(temperature) OVER (
    PARTITION BY city
    ORDER BY date
    ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
  ) AS moving_avg_temp
FROM weather_data
ORDER BY city, date;

This query calculates a 3-day moving average of temperature readings for each city, alongside the current temperature:

    date    |    city     | temperature |   moving_avg_temp
------------+-------------+-------------+---------------------
 2024-03-01 | Chicago     |         2.0 |  2.0000000000000000
 2024-03-02 | Chicago     |         3.5 |  2.7500000000000000
 2024-03-03 | Chicago     |         1.0 |  2.1666666666666667
 2024-03-01 | Los Angeles |        22.0 | 22.0000000000000000
 2024-03-02 | Los Angeles |        23.5 | 22.7500000000000000
 2024-03-03 | Los Angeles |        21.5 | 22.3333333333333333
 2024-03-01 | New York    |         5.5 |  5.5000000000000000
 2024-03-02 | New York    |         7.0 |  6.2500000000000000
 2024-03-03 | New York    |         6.5 |  6.3333333333333333
(9 rows)

The avg() function automatically ignores NULL values in its calculations. If all values are NULL, it returns NULL.

The avg() function returns a numeric value with the maximum precision and scale of any argument. You may want to use the round() function to control the number of decimal places in the result:

SELECT round(avg(temperature), 2) AS avg_temperature
FROM weather_data;

When working with large datasets, calculating averages can be resource-intensive, especially when combined with complex GROUP BY clauses or subqueries. Consider using materialized views or pre-aggregating data for frequently used averages for analytics applications.

• percentile_cont(): Calculates a continuous percentile value. It can be used to compute the median or other percentiles. Note that it is an ordered-set aggregate function and requires a WITHIN GROUP clause.
• mode(): Returns the most frequent value in a set. It is also an ordered-set aggregate function.

• PostgreSQL documentation: Aggregate Functions
• PostgreSQL documentation: Mathematical Functions and Operators

• array_agg
• count
• max
• sum

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